import argparse
import os
import sys
import json
from multiprocessing import cpu_count
import torch
try:
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
if torch.xpu.is_available():
from infer.modules.ipex import ipex_init
ipex_init()
except Exception: # pylint: disable=broad-exception-caught
pass
import logging
logger = logging.getLogger(__name__)
version_config_list = [
"v1/32k.json",
"v1/40k.json",
"v1/48k.json",
"v2/48k.json",
"v2/32k.json",
]
def singleton_variable(func):
def wrapper(*args, **kwargs):
if not wrapper.instance:
wrapper.instance = func(*args, **kwargs)
return wrapper.instance
wrapper.instance = None
return wrapper
@singleton_variable
class Config:
def __init__(self):
self.device = "cuda:0"
self.is_half = True
self.use_jit = False
self.n_cpu = 0
self.gpu_name = None
self.json_config = self.load_config_json()
self.gpu_mem = None
(
self.python_cmd,
self.listen_port,
self.iscolab,
self.noparallel,
self.noautoopen,
self.dml,
) = self.arg_parse()
self.instead = ""
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
@staticmethod
def load_config_json() -> dict:
d = {}
for config_file in version_config_list:
with open(f"configs/{config_file}", "r") as f:
d[config_file] = json.load(f)
return d
@staticmethod
def arg_parse() -> tuple:
exe = sys.executable or "python"
parser = argparse.ArgumentParser()
parser.add_argument("--port", type=int, default=7865, help="Listen port")
parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
parser.add_argument("--colab", action="store_true", help="Launch in colab")
parser.add_argument(
"--noparallel", action="store_true", help="Disable parallel processing"
)
parser.add_argument(
"--noautoopen",
action="store_true",
help="Do not open in browser automatically",
)
parser.add_argument(
"--dml",
action="store_true",
help="torch_dml",
)
cmd_opts = parser.parse_args()
cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
return (
cmd_opts.pycmd,
cmd_opts.port,
cmd_opts.colab,
cmd_opts.noparallel,
cmd_opts.noautoopen,
cmd_opts.dml,
)
# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
# check `getattr` and try it for compatibility
@staticmethod
def has_mps() -> bool:
if not torch.backends.mps.is_available():
return False
try:
torch.zeros(1).to(torch.device("mps"))
return True
except Exception:
return False
@staticmethod
def has_xpu() -> bool:
if hasattr(torch, "xpu") and torch.xpu.is_available():
return True
else:
return False
def use_fp32_config(self):
for config_file in version_config_list:
self.json_config[config_file]["train"]["fp16_run"] = False
with open(f"configs/{config_file}", "r") as f:
strr = f.read().replace("true", "false")
with open(f"configs/{config_file}", "w") as f:
f.write(strr)
with open("infer/modules/train/preprocess.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
with open("infer/modules/train/preprocess.py", "w") as f:
f.write(strr)
print("overwrite preprocess and configs.json")
def device_config(self) -> tuple:
if torch.cuda.is_available():
if self.has_xpu():
self.device = self.instead = "xpu:0"
self.is_half = True
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
if (
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
or "P40" in self.gpu_name.upper()
or "P10" in self.gpu_name.upper()
or "1060" in self.gpu_name
or "1070" in self.gpu_name
or "1080" in self.gpu_name
):
logger.info("Found GPU %s, force to fp32", self.gpu_name)
self.is_half = False
self.use_fp32_config()
else:
logger.info("Found GPU %s", self.gpu_name)
self.gpu_mem = int(
torch.cuda.get_device_properties(i_device).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
if self.gpu_mem <= 4:
with open("infer/modules/train/preprocess.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
with open("infer/modules/train/preprocess.py", "w") as f:
f.write(strr)
elif self.has_mps():
logger.info("No supported Nvidia GPU found")
self.device = self.instead = "mps"
self.is_half = False
self.use_fp32_config()
else:
logger.info("No supported Nvidia GPU found")
self.device = self.instead = "cpu"
self.is_half = False
self.use_fp32_config()
if self.n_cpu == 0:
self.n_cpu = cpu_count()
if self.is_half:
# 6G显存配置
x_pad = 3
x_query = 10
x_center = 60
x_max = 65
else:
# 5G显存配置
x_pad = 1
x_query = 6
x_center = 38
x_max = 41
if self.gpu_mem is not None and self.gpu_mem <= 4:
x_pad = 1
x_query = 5
x_center = 30
x_max = 32
if self.dml:
logger.info("Use DirectML instead")
if (
os.path.exists(
"runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
)
== False
):
try:
os.rename(
"runtime\Lib\site-packages\onnxruntime",
"runtime\Lib\site-packages\onnxruntime-cuda",
)
except:
pass
try:
os.rename(
"runtime\Lib\site-packages\onnxruntime-dml",
"runtime\Lib\site-packages\onnxruntime",
)
except:
pass
# if self.device != "cpu":
import torch_directml
self.device = torch_directml.device(torch_directml.default_device())
self.is_half = False
else:
if self.instead:
logger.info(f"Use {self.instead} instead")
if (
os.path.exists(
"runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
)
== False
):
try:
os.rename(
"runtime\Lib\site-packages\onnxruntime",
"runtime\Lib\site-packages\onnxruntime-dml",
)
except:
pass
try:
os.rename(
"runtime\Lib\site-packages\onnxruntime-cuda",
"runtime\Lib\site-packages\onnxruntime",
)
except:
pass
print("is_half:%s, device:%s" % (self.is_half, self.device))
return x_pad, x_query, x_center, x_max